In the age of rapidly increasing volumes of data, human experts have come to the urgent need to extract useful information from the huge amount of data. Knowldege discovery in databases has obtained much attention for researches and applications in business and in science. In this paper, we present a neuro-fuzzy approach using complex fuzzy sets (CFSs) for the problem of knowledge discovery. A CFS is an advanced fuzzy set, whose membership is complex-valued and characterized by an amplitude function and a phase function. The application of CFSs to the proposed complex neuro-fuzzy system (CNFS) can increase the functional mapping ability to find missing data for knowledge discovery. Moreover, we devise a hybrid learning algorithm to evolve the CNFS for modeling accuracy, combining the artificial bee colony algorithm and the recursive least squares estimator method. The proposed approach to knowledge discovery is tested through experimentation, whose results are compared with those by other approaches. The experimental results indicate that the proposed approach outperforms the compared approaches.